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Livestream: Step by Step: When and How to Use Stochastic Optimization
October 11 @ 8:30 pm - 9:30 pm
NOTE: Please sign-in at via the Livestream link below. Thank you!
This presentation will build on what you already know about parameter optimization to dive deeper into the world of stochastic optimization. In particular, we’ll explore stochastic gradient descent, simulated annealing, and particle swarm optimization – cultivating intuition on how these algorithms work, when they can be applied, what their underlying topologies look like, and how you can get the best performance out of them.
What to Know and Bring
If possible, bring models you’re working, so you can relate the content directly to what you’re building. I’ll be available afterwards (and by e-mail) to ensure you have what you need to implement the optimizers we discuss. Code examples will be presented in Python, but I’ll also provide libraries and resources in R.
About the Speaker – Siobhán Cronin
Siobhán Cronin is a machine learning engineer and scientist with a passion for computational intelligence.
She began her career in a neuroscience laboratory at Harvard Medical School, while moonlighting as a contemporary dancer. After several years of performing and NGO management, she returned to science to join the computational intelligence renaissance. She is currently building a predictive model for a transportation startup, collaborating on swarm intelligence optimization tools, co-producing a complexity modeling immersive study experience in partnership with Slow Research Lab in Amsterdam, and completing CS graduate coursework at Stanford.
Siobhán has published research in Brain & Cognition, Neuroreport, and Model View Culture, and presented at the Conference on Complex Systems, Conference on Cognitive Neuroscience, AlterConf, PyLadies, and Temple University. She is a member of Bay Area Women in Machine Learning and Data Science and IEEE Computational Intelligence Society (CIS), and coordinates a monthly a machine learning working group in San Francisco (ML Study Hall).
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